Generative adversarial network training and feature extraction for biometric authentication

ABSTRACT

Embodiments of the present invention provide a system for generative adversarial network training and feature extraction for biometric authentication. The system collects electronic biometric data of a user from one or more data sources, and stores the collected electronic biometric data as a biometric user account for the user in a personal NoSQL database library associated with the user. A generative adversarial neural network system then determines improved biometric feature selection and improved model refinements for existing biometric authentication models based on the biometric account for the user in the personal library associated with the user. The system can then determine user exposure levels for different authentication channels, including certain biometric authentication channels. A custom adversarial strategy for general adversarial network attacks is then established based on the user exposure levels to generate a biometric authentication process that is more accurate and secure.

BACKGROUND

Biometric authentication solutions provide improved security overstandard username, personal identification number, and password-basedsolutions, as these standard authentication solutions may be identifiedand extracted if exposed. Instead, biometric authentication solutionsare more complex and can be combined in multi-modal biometricauthentication steps such as face recognition, voice or speechrecognition, gait, behavioral biometrics, and the like.

However, malfeasant actors are developing techniques to target thecurrent generation of biometric solutions by compiling publiclyavailable data of people (e.g., images, videos, voice recordings, andthe like) to create three dimensional models of those people that arethen enriched with gestures that are then used to deceive biometricauthentication systems. Therefore, a need exists for a more robustbiometric authentication system with generative adversarial networktraining and feature extraction to provide a higher accuracy andsecurity in biometric authentication practices.

BRIEF SUMMARY

The following presents a summary of certain embodiments of theinvention. This summary is not intended to identify key or criticalelements of all embodiments nor delineate the scope of any or allembodiments. Its sole purpose is to present certain concepts andelements of one or more embodiments in a summary form as a prelude tothe more detailed description that follows.

Embodiments of the present invention address the above needs and/orachieve other advantages by providing apparatuses (e.g., a system,computer program product and/or other devices) and methods forgenerative adversarial network training and feature extraction forbiometric authentication. The system embodiments may comprise one ormore memory devices having computer readable program code storedthereon, a communication device, and one or more processing devicesoperatively coupled to the one or more memory devices, wherein the oneor more processing devices are configured to execute the computerreadable program code to carry out the invention. In computer programproduct embodiments of the invention, the computer program productcomprises at least one non-transitory computer readable mediumcomprising computer readable instructions for carrying out theinvention. Computer implemented method embodiments of the invention maycomprise providing a computing system comprising a computer processingdevice and a non-transitory computer readable medium, where the computerreadable medium comprises configured computer program instruction code,such that when said instruction code is operated by said computerprocessing device, said computer processing device performs certainoperations to carry out the invention.

For sample, illustrative purposes, system environments will besummarized. The system may involve collecting electronic biometric dataof a user from one or more data sources comprising social media systems,third party vendor systems, systems of known exposed data, and publicinformation space systems, wherein the electronic biometric data of theuser comprises at least one of image data, video data, and voicerecording data associated with the user. The system may then store thecollected electronic biometric data of the user as a biometric accountfor the user in a personal library associated with the user, wherein thepersonal library associated with the user comprises a real-time NoSQLdatabase.

Once the personal library storing the electronic biometric data of theuser has been created, the system may consolidate the database byidentify one or more inconsistencies in the stored electronic biometricdata of the user; removing the identified one or more inconsistencies inthe stored electronic biometric data of the user from the personallibrary, and then consolidating the stored electronic biometric data ofthe user within the personal library associated with the user, withoutthe identified one or more inconsistencies. The system may additionallyor alternatively dynamically update the stored electronic biometric dataof the user within the personal library associated with the user in realtime in response to determining that new or adjusted electronicbiometric data of the user is available from the one or more datasources.

They system may then cause a generative adversarial neural networksystem to determine improved biometric feature selection and improvedmodel refinements for existing biometric authentication models based onthe biometric account for the user in the personal library associatedwith the user. The system can then determine, based on the improvedfeature selection and the improved model refinements for the existingbiometric authentication models, user exposure levels for one or morebiometric authentication channels, combinations of biometricauthentication channels, and/or combinations of non-biometric andbiometric authentication channels. Next, the system may establish acustom adversarial strategy for generative adversarial network (“GAN”)attacks based on the determined user exposure levels for the one or morebiometric authentication channels, combinations of biometricauthentication channels, and/or combinations of non-biometric andbiometric authentication channels.

The system may establish the custom adversarial strategy for GAN attacksin a number of ways. For example, the system may change the existingbiometric authentication models comprising improved model refinements asdetermined by the generative adversarial neural network system byrequiring a biometric authentication action involving traditionally orpreviously unexposed biometric features or scenarios. Additionally oralternatively the system establishes the custom adversarial strategy bychanging the existing biometric authentication models comprisingimproved model refinements as determined by the generative adversarialneural network system by requiring a randomly selected authenticationaction involving changed biometric authentication conditions orinteraction patterns. In other embodiments, the system may establish acustom adversarial strategy for GAN attacks by changing weighted valuesof at least one of the one or more biometric authentication channels,and/or changing weighted values of at least one of the existingbiometric authentication models comprising improved model refinements asdetermined by the generative adversarial neural network system. In someembodiments, the system may establish the custom adversarial strategyfor GAN attacks by adding one or more additional authentication methodsto the existing biometric authentication models comprising improvedmodel refinements as determined by the generative adversarial neuralnetwork system, and/or requiring a stepped up level of authenticationfrom existing authentication models. Finally, in some embodiments, thesystem may establish a custom adversarial strategy for GAN attacks bydetermining whether received biometric authentication data from anindividual purporting to be the user matches a data pattern presentwithin a custom database of known GAN attack data.

Once the custom adversarial strategy for the generative adversarialnetwork has been established, the system may identify previous biometricauthentication sessions for the user from a historical user database,where the previous biometric authentication sessions involved theexisting biometric authentication models without the improved modelrefinements. The system may then evaluate received biometricauthentication data of the user for each of the previous biometricauthentication sessions for the user based on the custom adversarialstrategy for GAN attacks to identify potential exposures from previouslyunknown GAN attacks. In response to identifying a first previousbiometric authentication session that is associated with a previouslyunknown GAN attack, the system may tag the received biometricauthentication data of the user for that first previous biometricauthentication session as being associated with imitability and exposuremetrics.

The features, functions, and advantages that have been discussed may beachieved independently in various embodiments of the present inventionor may be combined with yet other embodiments, further details of whichcan be seen with reference to the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Having thus described embodiments of the invention in general terms,reference will now be made the accompanying drawings, wherein:

FIG. 1 provides a block diagram illustrating a system environment forgenerative adversarial network training and feature extraction forbiometric authentication, in accordance with an embodiment of theinvention;

FIG. 2 provides a block diagram illustrating the managing entity systemof FIG. 1, in accordance with an embodiment of the invention;

FIG. 3 provides a block diagram illustrating the biometricauthentication GAN system of FIG. 1, in accordance with an embodiment ofthe invention;

FIG. 4 provides a block diagram illustrating the authentication devicesystem of FIG. 1, in accordance with an embodiment of the invention;

FIG. 5 provides a flowchart for adversarial network training and featureextraction for biometric authentication, in accordance with anembodiment of the invention;

FIG. 6 provides a flowchart 600 of a system for generative adversarialnetwork training and feature extraction for biometric authentication, inaccordance with an embodiment of the invention; and

FIG. 7 provides a flowchart 700 that provides a description of thesystem for generative adversarial network training and featureextraction for biometric authentication, in accordance with anembodiment of the invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Embodiments of the present invention will now be described more fullyhereinafter with reference to the accompanying drawings, in which some,but not all, embodiments of the invention are shown. Indeed, theinvention may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. Where possible, any terms expressed in the singularform herein are meant to also include the plural form and vice versa,unless explicitly stated otherwise. Also, as used herein, the term “a”and/or “an” shall mean “one or more,” even though the phrase “one ormore” is also used herein. Furthermore, when it is said herein thatsomething is “based on” something else, it may be based on one or moreother things as well. In other words, unless expressly indicatedotherwise, as used herein “based on” means “based at least in part on”or “based at least partially on.” Like numbers refer to like elementsthroughout.

FIG. 1 provides a block diagram illustrating a system environment 100for generative adversarial network training and feature extraction forbiometric authentication, in accordance with an embodiment of theinvention. As illustrated in FIG. 1, the environment 100 includes amanaging entity system 200, a biometric authentication GAN system 300,one or more authentication device systems 400, biometric data sourcesystems 120, a biometric account library 130, and one or more thirdparty systems 140. One or more users 110 may be included in the systemenvironment 100. In some embodiments, the user(s) 110 of the systemenvironment 100 may be customers of a managing entity (e.g., a financialinstitution, an information security entity, or the like) associatedwith the managing entity system 200. As such, this system environment100 may be configured to receive biometric authentication data (andnon-biometric authentication data) from the user 110, via anauthentication device system 400, where the managing entity system 200determines whether the received authentication data from the user 110aligns with known and/or generated expected traits that are unique tothe user 110 by using the biometric authentication GAN system 300, thebiometric data source systems 120, the biometric account library 130,and/or one or more third party systems 140.

The managing entity system 200, the biometric authentication GAN system300, the authentication device system 400, the biometric data sourcesystems 120, the biometric account library 130, and/or the third partysystem 140 may be in network communication across the system environment100 through the network 150. The network 150 may include a local areanetwork (LAN), a wide area network (WAN), and/or a global area network(GAN). The network 150 may provide for wireline, wireless, or acombination of wireline and wireless communication between devices inthe network. In one embodiment, the network 150 includes the Internet.

The managing entity system 200 may be a system owned or otherwisecontrolled by a managing entity to perform one or more process stepsdescribed herein. In some embodiments, the managing entity is afinancial institution. In general, the managing entity system 200 isconfigured to communicate information or instructions with the biometricauthentication GAN system 300, the authentication device system 400, thebiometric data source systems 120, the biometric account library 130,and/or the third party system 140 across the network 150. For example,the managing entity system 200 may receive or extract biometric datafrom the biometric data source systems 120, store biometric data in thebiometric account library 130, cause the biometric authentication GANsystem 300 to analyze and update biometric authentication channels andstrategies, and communicate with the authentication device system 400 toauthenticate a user 110 based on the updated biometric authenticationchannels and strategies. Of course, the managing entity system 200 maybe configured to perform (or instruct other systems to perform) one ormore other process steps described herein. The managing entity system200 is described in more detail with respect to FIG. 2.

The biometric authentication GAN system 300 may be a system owned orcontrolled by the managing entity and/or a third party that specializesin machine learning, especially utilizing deep neural networkprocedures, to analyze biometric information of users (includingmultiple biometric channels and combinations of multiple biometricchannels) to identify potential areas of exposure in the authenticationof the user 110. In general, the biometric authentication GAN system 300is configured to communicate information or instructions with themanaging entity system 200, the biometric data source systems 120, thebiometric account library 130, the authentication device system 400,and/or the third party system 140 across the network 150. In someembodiments, the biometric authentication GAN system 300 is astand-alone system that communicates with these other systems anddatabases via the network 150, but in other embodiments at least aportion of the biometric authentication GAN system 300 is a component ofor otherwise controlled or managed by the managing entity system 200.The biometric authentication GAN system 300 may be configured to perform(or instruct other systems to perform) one or more other process stepsdescribed herein related to the analysis of biometric data of the user110, analyze received biometric data of individuals purporting to be theuser 110, identifying exposure levels across multiple userauthentication modalities, and the like. The biometric authenticationGAN system 300 is described in more detail with respect to FIG. 3.

The authentication device system 400 may be a system owned or controlledby the managing entity and/or a third party that specializes in userauthentication that includes one or more channels of biometricauthentication. In general, the authentication device system 400 isconfigured to communicate information or instructions with the managingentity system 200, the biometric authentication GAN system 300, thebiometric data source systems 120, the biometric account library 130,and/or the third party system 140 across the network 150. For example,the authentication device system 400 may prompt the user 110 to providea biometric authentication action (e.g., fingerprint scan, facial scan,facial video monitoring, gesture video monitoring, voice recordingmonitoring, or the like). Of course, the authentication device system400 may be configured to perform (or instruct other systems to perform)one or more other process steps described herein. The authenticationdevice system 400 is described in more detail with respect to FIG. 4.

The biometric data source systems 120 may comprise one or more remote orlocal servers or other systems that provide, acquire, aggregate, orotherwise store biometric data that can be used to generateauthentication templates of biometric features of users. For example,one or more of the biometric data source systems 120 may comprise socialmedia systems, where the associated biometric data source systems 120have paired or otherwise linked an image, video, or voice recording (ora portion of the same) with a biometric feature (e.g., the face) of anindividual (e.g., the user 110). In another example, one or more of thebiometric data source systems 120 may comprise third party biometricdata vendor systems that aggregate user biometric data from otherinstitutions, directly from the users themselves, from social mediasites, and the like. These third party vendor systems may additionallyapply some preliminary analysis on the biometric data to clarify whichdata is associated with which user, or to identify a degree ofconfidence or strength with respect to raw biometric data points fromtheir original sources.

The biometric data source systems 120 may additionally or alternativelyinclude one or more databases (e.g., an internal database, a databasemanaged by a government entity or another third party entity) ofbiometric data that is known to have been exposed. This biometric datathat is known to have been exposed in the past may be utilized toidentify when malfeasant actors are attempting to use the exposed data.Finally, in some embodiments, one or more of the biometric data sourcesystems 120 may comprise a public information space system like theInternet, where the managing entity system 200 and/or the biometricauthentication GAN system can trawl the Internet to identify additionalsources of biometric data for particular individuals (e.g., the user110). These biometric data source systems may provide the biometric dataas image data, video data, voice recording data, scanning data, radardata, weight data, or the like.

The biometric account library may comprise a network communicationinterface, a processing device, and one or more memory devices, wherethe processing devices are configured to perform certain actions withthe memory devices and communicate these actions to the rest of thenetwork 150 through the network communication interface. The biometricaccount library 130 may include sets of biometric data (e.g., biometricdata for each biometric authentication channel) for each customer (e.g.,the user 110) of the managing entity system, and may include additionalauthentication information of the user, false biometric information ofthe user (e.g., biometric information that was previously presented anddetermined to be illegitimate), and the like.

The third party system 140 may be any system that provides anyadditional or supplemental actions to enable the generative adversarialnetwork training and feature extraction for biometric authentication ofthe user 110.

FIG. 2 provides a block diagram illustrating the managing entity system200, in greater detail, in accordance with embodiments of the invention.As illustrated in FIG. 2, in one embodiment of the invention, themanaging entity system 200 includes one or more processing devices 220operatively coupled to a network communication interface 210 and amemory device 230. In certain embodiments, the managing entity system200 is operated by a first entity, such as a financial institution,while in other embodiments, the managing entity system 200 is operatedby an entity other than a financial institution.

It should be understood that the memory device 230 may include one ormore databases or other data structures/repositories. The memory device230 also includes computer-executable program code that instructs theprocessing device 220 to operate the network communication interface 210to perform certain communication functions of the managing entity system200 described herein. For example, in one embodiment of the managingentity system 200, the memory device 230 includes, but is not limitedto, a network server application 240, an authentication application 250which includes authentication data 252. The computer-executable programcode of the network server application 240 and the authenticationapplication 250 may instruct the processing device 220 to performcertain logic, data-processing, and data-storing functions of themanaging entity system 200 described herein, as well as communicationfunctions of the managing entity system 200.

The network server application 240 and the authentication application250 are configured to invoke or use the authentication data 252, and thelike when communicating through the network communication interface 210with the biometric authentication GAN system 300, the biometric datasource systems 120, the biometric account library 130, and/or theauthentication device system 400 to cause the biometric authenticationGAN system to establish improved authentication parameters and/orpractices and to authenticate the user 110 with the improvedauthentication parameters and/or practices.

FIG. 3 provides a block diagram illustrating the biometricauthentication GAN system 300, in greater detail, in accordance withembodiments of the invention. As illustrated in FIG. 3, in oneembodiment of the invention, the biometric authentication GAN system 300includes one or more processing devices 320 operatively coupled to anetwork communication interface 310 and a memory device 330. In certainembodiments, the biometric authentication GAN system 300 is operated bya first entity, such as a financial institution, while in otherembodiments, the biometric authentication GAN system 300 is operated byan entity other than a financial institution.

It should be understood that the memory device 330 may include one ormore databases or other data structures/repositories. The memory device330 also includes computer-executable program code that instructs theprocessing device 320 to operate the network communication interface 310to perform certain communication functions of the biometricauthentication GAN system 300 described herein. For example, in oneembodiment of the biometric authentication GAN system 300, the memorydevice 330 includes, but is not limited to, a network server application340, a machine learning application 350 which includes GAN data 352 andbiometric account data 354, and other computer-executable instructionsor other data. The computer-executable program code of the networkserver application 340 and/or the machine learning application 350 mayinstruct the processing device 320 to perform certain logic,data-processing, and data-storing functions of the biometricauthentication GAN system 300 described herein, as well as communicationfunctions of the biometric authentication GAN system 300.

The machine learning application 350 may be associated with a machinelearning system which may include a knowledge base (e.g., the GAN data352, the biometric account data 354, and/or the biometric data sourcesystems 120), a set of biometric authentication analysis rules (e.g.,rules based on a learning classifier system, rules based on anassociation rule learning system, or the like), and any other sets ofdata, rules, guidelines, boundaries, and any other information that canbe generate models of biometric authentication data from public dataand/or to test such models against known biometric data, as describedherein.

This machine learning system may comprise a deep learning system like adeep neural network-based system in addition to other machine learningfunctions like decision trees and regression techniques. In someembodiments, this deep neural network may comprise 3, 4, or more layers,and may comprise one or more of an autoencoder, a multilayer perceptron(“MLP”) a recurrent neural network (“RNN”), a convolutional deep neuralnetwork (“CNN”), a Boltzmann machine, and the like.

The network server application 340 and the machine learning application350 are configured to invoke or use the GAN data 352, the biometricaccount data 354, and the like when communicating through the networkcommunication interface 310 with the managing entity system 200, thebiometric data source systems 120, the biometric account library 130,and/or the authentication device system 400 to perform one or more ofthe biometric authentication steps, the biometric authentication channelanalysis steps, the biometric authentication modality analysis steps,biometric account generation steps, and the like.

FIG. 4 provides a block diagram illustrating a authentication devicesystem 400 of FIG. 1 in more detail, in accordance with embodiments ofthe invention. In one embodiment of the invention, the authenticationdevice system 400 is a mobile telephone. However, it should beunderstood that a mobile telephone is merely illustrative of one type ofauthentication device system 400 that may benefit from, employ, orotherwise be involved with embodiments of the present invention and,therefore, should not be taken to limit the scope of embodiments of thepresent invention. Other types of computing devices may include portabledigital assistants (PDAs), pagers, mobile televisions, electronic mediadevices, desktop computers, workstations, laptop computers, cameras,video recorders, audio/video player, radio, GPS devices, wearabledevices, Internet-of-things devices, augmented reality devices, virtualreality devices, automated teller machine devices, electronic kioskdevices, fingerprint scanners, retinal scanners, voice recordingdevices, motion sensing devices, scales, or any combination of theaforementioned.

Some embodiments of the authentication device system 400 include aprocessor 410 communicably coupled to such devices as a memory 420, useroutput devices 436, user input devices 440, a network interface 460, apower source 415, a clock or other timer 450, a camera 480, and apositioning system device 475. The processor 410, and other processorsdescribed herein, generally include circuitry for implementingcommunication and/or logic functions of the authentication device system400. For example, the processor 410 may include a digital signalprocessor device, a microprocessor device, and various analog to digitalconverters, digital to analog converters, and/or other support circuits.Control and signal processing functions of the authentication devicesystem 400 are allocated between these devices according to theirrespective capabilities. The processor 410 thus may also include thefunctionality to encode and interleave messages and data prior tomodulation and transmission. The processor 410 can additionally includean internal data modem. Further, the processor 410 may includefunctionality to operate one or more software programs, which may bestored in the memory 420. For example, the processor 410 may be capableof operating a connectivity program, such as a web browser application422. The web browser application 422 may then allow the authenticationdevice system 400 to transmit and receive web content, such as, forexample, location-based content and/or other web page content, accordingto a Wireless Application Protocol (WAP), Hypertext Transfer Protocol(HTTP), and/or the like.

The processor 410 is configured to use the network interface 460 tocommunicate with one or more other devices on the network 150. In thisregard, the network interface 460 includes an antenna 476 operativelycoupled to a transmitter 474 and a receiver 472 (together a“transceiver”). The processor 410 is configured to provide signals toand receive signals from the transmitter 474 and receiver 472,respectively. The signals may include signaling information inaccordance with the air interface standard of the applicable cellularsystem of a wireless network. In this regard, the authentication devicesystem 400 may be configured to operate with one or more air interfacestandards, communication protocols, modulation types, and access types.By way of illustration, the authentication device system 400 may beconfigured to operate in accordance with any of a number of first,second, third, and/or fourth-generation communication protocols and/orthe like. For example, the authentication device system 400 may beconfigured to operate in accordance with second-generation (2G) wirelesscommunication protocols IS-136 (time division multiple access (TDMA)),GSM (global system for mobile communication), and/or IS-95 (codedivision multiple access (CDMA)), or with third-generation (3G) wirelesscommunication protocols, such as Universal Mobile TelecommunicationsSystem (UMTS), CDMA2000, wideband CDMA (WCDMA) and/or timedivision-synchronous CDMA (TD-SCDMA), with fourth-generation (4G)wireless communication protocols, with LTE protocols, with 4GPPprotocols and/or the like. The authentication device system 400 may alsobe configured to operate in accordance with non-cellular communicationmechanisms, such as via a wireless local area network (WLAN) or othercommunication/data networks.

As described above, the authentication device system 400 has a userinterface that is, like other user interfaces described herein, made upof user output devices 436 and/or user input devices 440. The useroutput devices 436 include a display 430 (e.g., a liquid crystal displayor the like) and a speaker 432 or other audio device, which areoperatively coupled to the processor 410.

The user input devices 440, which allow the authentication device system400 to receive data from a user such as the user 110, may include any ofa number of devices allowing the authentication device system 400 toreceive data from the user 110, such as a keypad, keyboard,touch-screen, touchpad, microphone, mouse, joystick, other pointerdevice, button, soft key, and/or other input device(s). The userinterface may also include a camera 480, such as a digital camera.

The authentication device system 400 may also include a positioningsystem device 475 that is configured to be used by a positioning systemto determine a location of the authentication device system 400. Forexample, the positioning system device 475 may include a GPStransceiver. In some embodiments, the positioning system device 475 isat least partially made up of the antenna 476, transmitter 474, andreceiver 472 described above. For example, in one embodiment,triangulation of cellular signals may be used to identify theapproximate or exact geographical location of the authentication devicesystem 400. In other embodiments, the positioning system device 475includes a proximity sensor or transmitter, such as an RFID tag, thatcan sense or be sensed by devices known to be located proximate amerchant or other location to determine that the authentication devicesystem 400 is located proximate these known devices. The positioningsystem device 475 may play a crucial role in transmitting locationinformation associated with the authentication device system 400 fordetermining when the authentication device system 400 is at or is inclose proximity to known or expected location of a user that is beingauthenticated.

The authentication device system 400 further includes a power source415, such as a battery, for powering various circuits and other devicesthat are used to operate the authentication device system 400.Embodiments of the authentication device system 400 may also include aclock or other timer 450 configured to determine and, in some cases,communicate actual or relative time to the processor 410 or one or moreother devices.

The authentication device system 400 also includes a memory 420operatively coupled to the processor 410. As used herein, memoryincludes any computer readable medium (as defined herein below)configured to store data, code, or other information. The memory 420 mayinclude volatile memory, such as volatile Random Access Memory (RAM)including a cache area for the temporary storage of data. The memory 420may also include non-volatile memory, which can be embedded and/or maybe removable. The non-volatile memory can additionally or alternativelyinclude an electrically erasable programmable read-only memory (EEPROM),flash memory or the like.

The memory 420 can store any of a number of applications which comprisecomputer-executable instructions/code executed by the processor 410 toimplement the functions of the authentication device system 400 and/orone or more of the process/method steps described herein. For example,the memory 420 may include such applications as a conventional webbrowser application 422 and/or an authentication application 421 (or anyother application provided by the managing entity system 200). Theseapplications also typically instructions to a graphical user interface(GUI) on the display 430 that allows the user 110 to interact with theauthentication device system 400, the managing entity system 200, and/orother devices or systems. In other embodiments of the invention, theuser 110 interacts with the managing entity system 200 or the resourceaccumulation system 400 via the web browser application 422 in additionto, or instead of, the authentication application 421.

The authentication application 421 may be configured to receiveinstructions from the managing entity system 200 and/or the biometricauthentication GAN system 300 to cause features of the authenticationdevice system 400 to perform steps for obtaining and transmittingbiometric authentication information of the user. For example, themanaging entity system 200 may cause the authentication application 421to turn on or otherwise activate the camera 480 to acquire images and/orvideo of an associated user, and/or to turn on or otherwise activate thea microphone of the user input devices 440 to acquire voice dataassociated with the associated user.

The memory 420 can also store any of a number of pieces of information,and data, used by the authentication device system 400 and theapplications and devices that make up the authentication device system400 or are in communication with the authentication device system 400 toimplement the functions of the authentication device system 400 and/orthe other systems described herein. For example, the memory 420 mayinclude such data as the user's biometric data, the user's non-biometricdata, and the like.

Referring now to FIG. 5, a flowchart is provided to illustrate oneembodiment of a process 500 for generative adversarial network trainingand feature extraction for biometric authentication, in accordance withembodiments of the invention.

The process 500 described herein is configured to address, mitigate,and/or prevent certain generative adversarial network (“GAN”) attacksassociated with falsified biometric data. The problem is that malfeasantactors acquire public images and videos of an individual, and generate athree-dimensional model of the face (including face texture, gazeinformation, and the like). The modeling may comprise facial landmarkextraction, three-dimensional model reconstruction, image-basedtexturing, and gaze correction. The malfeasant actors can also addgestures to the model (e.g., express animation), including facegestures, eye gestures (e.g., winking, blinking, looking in differentdirections, or the like) hand gestures, head gestures, and the like. Themalfeasant actors can then try to use this generated model to pass asthe individual in biometric authentication scenarios. Similarly, amalfeasant actor may utilize a GAN to create a digital copy or replicaof a person with a deep fake video.

To address this challenge, this process 500 described herein provides anovel approach to prevent the biometric authentication imitationtechniques by using publicly available data (e.g., the same data thatmalfeasant actors could use) from the Internet, social media sources, orother public data sources to build a personal biometric account library.This personal biometric account library is then used to trainadversarial neural networks to build biometric models that would besimilar or identical to the models created by malfeasant parties.Resulting adversarial neural networks are then used to co-train,challenge, and/or refine the biometrics neural network solutions, whichmay include the extraction of unique features in the associatedbiometrics process (e.g., by specifically zooming into the uniquecharacteristics that have not been controlled by the generativeadversarial network). The authentication strategy for the associateduser may be dynamically adjusted such that the weightings of theassociated features in the main biometrics engine are adjusted (e.g.,randomly switching to other biometrics modality combinations, and/oradversarial interactions are initiated using out-of-band authenticationwith a trusted device). Custom interaction methods can be developed withthe customer to minimize the exposure attacks, and login data can beevaluated based on adversarial network result similarities.

In some embodiments, the process 500 may include block 502, where thesystem collects electronic biometric data of a user from one or moredata sources comprising social media systems, third party vendorsystems, systems of known exposed data, and public information spacesystems, where the electronic biometric data of the user is image data,video data, and/or voice recording data associated with the user. Otherexamples of biometric data that can be acquired include, but are notlimited to, face recognition data, retinal image data, iris data, speechpattern recognition data, gait (e.g., walking style) data, weight data,gesture data, veins of eye data, shape of hand data, shape of ears data,and the like.

In some embodiments, the process 500 includes block 504, where thesystem stores the collected electronic biometric data of the user as abiometric account for the user in a personal library associated with theuser, where the personal library is a real-time NoSQL database.

The process 500 may include additional steps to consolidate the storedelectronic biometric data of the user. As such, the system may identify,within the personal library associated with the user, one or moreinconsistencies in the stored electronic biometric data of the user. Thesystem may then remove the identified one or more inconsistencies in thestored electronic biometric data of the user from the personal libraryassociated with the user. The system can then consolidate the storedelectronic biometric data of the user within the personal libraryassociated with the user, without the identified one or moreinconsistencies.

This personal library can be dynamically updated over time and/or inreal time. As such, the system may dynamically update the storedelectronic biometric data of the user within the personal libraryassociated with the user in real time in response to determining thatnew or adjusted electronic biometric data of the user is available fromthe one or more data sources.

Additionally, in some embodiments, the process 500 includes block 506,where the system causes a generative adversarial neural network systemto determine improved biometric feature selection and improved modelrefinements for existing biometric authentication models on thebiometric account for the user in the personal library associated withthe user. For example, the generative adversarial neural network systemmay determine that the generated biometric features are not especiallystrong at identifying and/or replicating a few particular faciallandmarks for a user, and therefore, may determine that these particularfacial landmarks should carry a greater deal of weight and influence infuture biometric authentication analyses.

The process 500 may also include block 508, where the system determines,based on the improved feature selection and the improved modelrefinements for the existing biometric authentication models, userexposure levels for biometric authentication channels, combinations ofbiometric authentication channels, or combinations of biometric andnon-biometric authentication channels.

In some embodiments, the process 500 includes block 510, where thesystem establishes a custom adversarial strategy for generativeadversarial (“GAN”) attacks based on the determined user exposure levelsfor the one or more biometric authentication channel, and/orcombinations of biometric and non-biometric authentication channels.

The establishment of the custom adversarial strategy for GAN attacks canbe accomplished in a number of ways, including combinations of thefollowing disclosed techniques. As a first example, the system maychange establish a custom adversarial strategy to combat GAN attacks ofbiometric authentication by changing the existing biometricauthentication models comprising improved model refinements asdetermined by the generative adversarial neural network system, byrequiring the execution of a biometric authentication action involvingtraditionally or previously unexposed biometric features or scenarios.

Using the example above regarding a few particular facial landmarkpoints, the system may adjust a biometric authentication action fromsimply having the user present the user's face to a camera for facialrecognition, to zooming into the area(s) of the user's face with thesefew particular facial landmark points that have been determined to bedifficult to replicate with GAN attacks.

Alternatively, the system may prompt the user to perform a gesture(e.g., smile, wink, turn head, or the like) in a manner that adjusts thefew particular facial landmark points, as it has been determined thatGAN attacks have difficulty in imitating these gestures. Similarly, thesystem may prompt the user to turn around (i.e., show the back of thehead, which may not have been generated), move side to side, orotherwise significantly adjust the location of the user, as a digitalimitation of the user may not be sophisticated enough to meet thisbiometric requirement.

Furthermore, if the system has control over the authentication device ofthe user and/or other features associated with the building or structurethat the user is in, the system can cause a lighting change to takeplace (e.g., switch which lights are on, change a coloring of thelights, cause an authenticating mobile device to change a color of thedisplay in a manner that should illuminate the user's face, or thelike). In this way, the system can take steps to challenge the weakestaspects of the digital biometric imitation strategies, based on theweaknesses identified for the internally developed GAN attacks.

In another example, the system may establish the custom adversarialstrategy for GAN attacks by changing the existing biometricauthentication models comprising improved model refinements asdetermined by the generative adversarial neural network system byrequiring a randomly selected authentication action involving changedbiometric authentication conditions or interaction patterns. Because theGAN attacks can become more life-like through a pre-established andgenerated simulation (a time-consuming process), the system may randomlyadjust which authentication actions should be performed by the user toauthenticate the user, thereby making it unlikely or impossible for amalfeasant actor to generate a deeper imitation of the user ahead oftime.

Furthermore, the system may establish a custom adversarial strategy forGAN attacks by changing weighted values of at least one of the one ormore biometric authentication channels. These changed weightings of theindividual biometric authentication channels may be based ondeterminations as to which of the individual biometric channels are thestrongest, and the weakest, from the internally generated and tested GANattack structures. For example, if the system determines that theretinal scan model generated from the publicly available informationclosely matches the known (e.g., private data) retinal scan information,the system may significantly decrease (or cut-out all together) theretinal scan authentication channel. However, if the system determinesthat the voice authentication model and the waving authentication model,as generated from the publicly available biometric data, were difficultto match with real-world or gold-standard authentication data for theuser (e.g., privately acquired data), then the system may strengthen theweightings for these authentication channels for the overallauthentication determination process.

Similarly, the system may change weighted values of at least one of theexisting biometric authentication models comprising improved modelrefinements as determined by the generative adversarial neural networksystem. The authentication models may comprise multiple authenticationchannels, and therefore changing an authentication model may compriseadding a new authentication channel to the model, adjustingauthentication requirements of one of the underlying authenticationchannels, replacing one or more of the authentication channels withinthe model, or the like.

In some embodiments, the system may establish the custom adversarialstrategy for GAN attacks by adding one or more additional authenticationmethods to the existing biometric authentication models comprisingimproved model refinements as determined by the generative adversarialneural network system. These additional authentication methods maycomprise one or more additional biometric authentication channels (e.g.,adding a voice recording to a facial recognition authenticationchannel). However, the additional authentication method(s) may includenon-biometric authentication channels as well. For example, the systemmay require the user, in addition to simply providing a voice sample, tospeak out a randomly generated code provided to a secure computingdevice of the user, to speak out a password or code-word of the user, orthe like. Of course, the system could also request a typed-in passwordas an additional authentication method. These examples are meant to benon-limiting, as any combination of biometric and/or non-biometricauthentication channels can be added to improve the overallauthentication process.

Similarly, the system may require a stepped up level of authenticationfrom existing authentication models (e.g., non-biometric authenticationmodels). For example, if the system determines that a user is exposed ata certain level for facial recognition authentication, the system mayrequire this user to provide a facial video authentication when a facialrecognition authentication is standard practice. Additionally oralternatively, the system may require the user to provide additional,more stringent, authentication credentials (e.g., a passcode orpassword, a two-factor authentication code, or the like).

At least a portion of the established custom adversarial strategy forGAN attacks can be based on a determination as to whether receivedbiometric authentication data form an individual purporting to be theuser matches a data pattern present within a custom database of knownGAN attack data. The system may maintain a database of known GANattacks, and the machine learning system may analyze this database toidentify patterns in such GAN attacks. The system can then detect thesame patterns in new biometric authentication attempts and automaticallydeny the authentication in response to determining the pattern match.

The system may dynamically update its biometric authentication GANsystem, and the generative adversarial neural network system inparticular, to identify previous biometric authentication sessions forthe user from a historical user database (e.g., the biometric accountlibrary 130). The system can then evaluate received biometricauthentication data of the user for each of the previous biometricauthentication sessions for the user based on the custom adversarialstrategy for GAN attacks to identify potential exposures from previouslyunknown GAN attacks.

In response to identifying a first previous biometric authenticationsession that is associated with a previously unknown GAN attack, thesystem may tag the received biometric authentication data of the userfor that first previous biometric authentication session as beingassociated with imitability and exposure metrics.

While the process 500 of FIG. 5 is described with respect to biometricauthentication channels and modalities, as well as with combinations ofnon-biometric authentication channels and modalities, it should be knownthat this type of analysis can be performed for other visioning systemsor personal expression systems. For example, the system may analyze thepublicly available social media feeds produced by a particular user overtime and identify the information including, but not limited to sentencestructuring (e.g., commonly-used grammar schemes), common spellingerrors, vocabulary usage (e.g., extent of vocabulary, frequency ofvocabulary), timing of social media posts, and the like.

These publicly available pieces of information can be accumulated andused to model the human interactions, expressions, and/or habits of theuser within the social media. As such, the system may cause a GAN togenerate hypothetical models of the user's expressions, social mediaposts, non-social media communications, written letters, and/or thelike, and then to determine exposure values for individual elements ofthe generated user models. The system can then prevent any decisioning(e.g., authentication of a user, acceptance of instructions from asocial media account, approvals from a social media account, agreementsassociated with an account, or the like).

Turning now to FIG. 6, a process 600 of a system for generativeadversarial network training and feature extraction for biometricauthentication, in accordance with one or more embodiments of theinvention. As illustrated in FIG. 6, the process 600 may include block602, where biometrics and other data from multiple public and privatedata sources are collected (e.g., social media tagged images, publicvideos, and online photos). As new data is scanned or otherwise receivedfrom public or private sources, this pool of data is continuouslyupdated to provide a complete source of data in real time.

The collected data is then enriched with internal data and other datasources (internal and/or external) to reach a quality required forneural network training, as shown at block 604. The process 600 may thenproceed to block 606, where enriched biometrics data is used to trainadversarial networks for authentication data generation, where thistraining is conducted for each biometrics channel or multi-modalbiometrics channel. Next, as shown at block 608, GAN networks are usedto challenge the main model (e.g., the real-time biometrics model) forauthentication decisions to extract unique features from the data.

As shown at block 610, the system then checks the difference (i.e., thedelta) between the metrics identified through the GAN networks and theknown, or main biometrics. As shown at block 610, if the biometricauthentication data has a low match with the determinations of the GANnetworks (i.e., a small delta), then the biometric methods or modalitiesare determined to have a higher match with the authentication data (orat least a matching amount that is above a predetermined threshold). Assuch, the process 600 may continue to block 614 where any uniquefeatures in the differentiation are calculated. As shown at block 616,the system may adjust training and data acquisition strategies towardsthese differentiating features.

Moving back to block 610, if the biometric authentication data isdetermined to have a high match with the determinations of the GANnetworks, then the process 600 proceeds to block 618, where the overallauthentication strategy is adjusted. The adjustment of theauthentication strategy is described in some detail with respect toblock 510, but some examples of adjustments to biometric and/or overallauthentication strategies or modalities are provided here in block 618.For example, the system may adjust the authentication strategy for theassociated user by changing the weightings of the corresponding channel(e.g., specific biometric channels like fingerprints, voiceidentification codes, retinal scans, or the like). In this way, thesystem can put a greater emphasis on specific biometric channels thathave a lesser likelihood of exposure, based on the determinations of theGAN networks.

As another example, the system may change the user's authentication todifferent multi-modal biometrics channels, using the evaluation of theindividual channels in earlier steps. In another example, adversarialchanges can be injected to the authentication process to extract moredata on the differentiating features such that it will be more difficultfor a malfeasant to replicate the biometric features of the user.

In some embodiments, the system may simply implement random changes tothe authentication process, as the random nature of these changes willbe difficult or impossible for a malfeasant to predict. In suchembodiments, the random changes to the authentication process may remainrandom for subsequent authentication processes, such that a potentialmalfeasant will not be able to adjust its techniques to mimic thechanges to the authentication process.

Of course, other authentication strategy processes can be added to,combined with, or used instead of the described authenticationadjustment strategies of block 618.

As shown at block 620, the system may initiate an adversarialauthentication (e.g., through an out-of-band channel such that the datadoes not affect or diminish any in-bound data). The process 600 may thenanalyze retrieved data for adversarial training, as shown at block 620.For example, each individual cross-channel biometrics data is analyzedfor exposure determination (e.g., determining an exposure value). Theresulting exposure account is stored in a account of the user (e.g.,within the biometric account library 130 of FIG. 1) to optimizesubsequent authentication processes for this user. The machine learningsystem continuously learns through GAN and impersonation training witheach new biometric data point recovered in the public or externalprivate data sources.

FIG. 7 illustrates a process 700 that provides a description of thesystem for generative adversarial network training and featureextraction for biometric authentication, in accordance with one or moreembodiments of the invention. The process 700 may begin (and continue toupdate in real time) by receiving, extracting, or otherwise obtainingbiometric data and other authentication data of a user from a pluralityof data sources which may include public data sources 702, social mediasources 704, known compromised or high exposure data 706, and/or datafrom other third party sources 708. The process 700 continues with block710, where the system updates the account data of the user in real timebased on the public and private data sources. Next, the process 700turns to block 712, where the system consolidates data from internal andexternal data sources 712. The system may then update its GANfunctionality with the latest data (e.g., in real time, or near realtime). Once the system has updated the GAN functionality with the latestdata, the process 700 may make an exposure assessment on one or more ofthe biometric authentication channels of the user, based on this data,as indicated at block 716. This exposure assessment may be based onauthentication model results as well as the proximity of previousauthentication results as compared to the GAN results.

In embodiments where the exposure assessment determines that there is alow or insignificant concern for exposure for a particular biometricauthentication feature and/or channel, the process 700 reverts back toblock 710 until more account data associated with the user is obtained,such that the system continuously checks to ensure that the biometricfeatures or authentication channels that have been determined to beassociated with a low exposure remain as having low exposure. However,if the exposure assessment of block 716 determined that at least onebiometric channel, authentication modality, or user account data wasassociated with a high exposure level (e.g., at or above a predeterminedthreshold value), then the process 700 would move to blocks 718, 720,and/or 722.

As shown in block 718, the system may update an authentication model forimproved accuracy, security, and unique features of the identificationdata. As shown in block 720, the system may update an adversarialstrategy for interactions and/or data usage with the user. Additionally,as shown at block 722, the system may update authentication metricsand/or channel weightings as part of the updated authenticationtechnique.

The process 700 may then continue to block 724, where the systemdetermines whether the changes made in blocks 718, 720, and 722 areadequate based on the known user data, the previous authenticationtechniques, and the exposure assessment of block 716. If the systemdetermines that the changes are adequate, then the process 700 mayreturn to block 710 to be tested again as new user biometric accountdata is received.

However, if the system determines that the changes in block 724 were notadequate, then the process 700 may proceed to block 726, where thesystem updates the overall authentication strategy. Examples of how thesystem can update the overall authentication strategy are provided atblock 728, which provides a library strategy and look-up database or setof information that the system can use to update the authenticationstrategy. For example, the system can incorporate multi-factor,out-of-band authentication techniques to improve the authenticationstrategy. Additionally or alternatively, the system can rely onadditional secure passwords to improve the authentication techniques.Furthermore, the system may switch its authentication channels to other,less-exposed modalities, and change the overall multi-modal combinationof authentication channels to one the is less-exposed than the currentmodality. Additionally or alternatively, the system may rely on trustedhistorical data (e.g., videos taken in financial institution brancheswith the identity of the user being verified during, before, orimmediately after the video was taken), and improving the scoring orweighting of the user's biometric data from this recording, as comparedto biometric data received from other, less-trusted sources.

As will be appreciated by one of skill in the art, the present inventionmay be embodied as a method (including, for example, acomputer-implemented process, a business process, and/or any otherprocess), apparatus (including, for example, a system, machine, device,computer program product, and/or the like), or a combination of theforegoing. Accordingly, embodiments of the present invention may takethe form of an entirely hardware embodiment, an entirely softwareembodiment (including firmware, resident software, micro-code, and thelike), or an embodiment combining software and hardware aspects that maygenerally be referred to herein as a “system.” Furthermore, embodimentsof the present invention may take the form of a computer program producton a computer-readable medium having computer-executable program codeembodied in the medium.

Any suitable transitory or non-transitory computer readable medium maybe utilized. The computer readable medium may be, for example but notlimited to, an electronic, magnetic, optical, electromagnetic, infrared,or semiconductor system, apparatus, or device. More specific examples ofthe computer readable medium include, but are not limited to, thefollowing: an electrical connection having one or more wires; a tangiblestorage medium such as a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a compact discread-only memory (CD-ROM), or other optical or magnetic storage device.

In the context of this document, a computer readable medium may be anymedium that can contain, store, communicate, or transport the programfor use by or in connection with the instruction execution system,apparatus, or device. The computer usable program code may betransmitted using any appropriate medium, including but not limited tothe Internet, wireline, optical fiber cable, radio frequency (RF)signals, or other mediums.

Computer-executable program code for carrying out operations ofembodiments of the present invention may be written in an objectoriented, scripted or unscripted programming language such as Java,Perl, Smalltalk, C++, or the like. However, the computer program codefor carrying out operations of embodiments of the present invention mayalso be written in conventional procedural programming languages, suchas the “C” programming language or similar programming languages.

Embodiments of the present invention are described above with referenceto flowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products. It will be understood thateach block of the flowchart illustrations and/or block diagrams, and/orcombinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer-executable program codeportions. These computer-executable program code portions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce aparticular machine, such that the code portions, which execute via theprocessor of the computer or other programmable data processingapparatus, create mechanisms for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

These computer-executable program code portions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the code portions stored in the computer readablememory produce an article of manufacture including instructionmechanisms which implement the function/act specified in the flowchartand/or block diagram block(s).

The computer-executable program code may also be loaded onto a computeror other programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process such that the codeportions which execute on the computer or other programmable apparatusprovide steps for implementing the functions/acts specified in theflowchart and/or block diagram block(s). Alternatively, computer programimplemented steps or acts may be combined with operator or humanimplemented steps or acts in order to carry out an embodiment of theinvention.

As the phrase is used herein, a processor may be “configured to” performa certain function in a variety of ways, including, for example, byhaving one or more general-purpose circuits perform the function byexecuting particular computer-executable program code embodied incomputer-readable medium, and/or by having one or moreapplication-specific circuits perform the function.

Embodiments of the present invention are described above with referenceto flowcharts and/or block diagrams. It will be understood that steps ofthe processes described herein may be performed in orders different thanthose illustrated in the flowcharts. In other words, the processesrepresented by the blocks of a flowchart may, in some embodiments, be inperformed in an order other that the order illustrated, may be combinedor divided, or may be performed simultaneously. It will also beunderstood that the blocks of the block diagrams illustrated, in someembodiments, merely conceptual delineations between systems and one ormore of the systems illustrated by a block in the block diagrams may becombined or share hardware and/or software with another one or more ofthe systems illustrated by a block in the block diagrams. Likewise, adevice, system, apparatus, and/or the like may be made up of one or moredevices, systems, apparatuses, and/or the like. For example, where aprocessor is illustrated or described herein, the processor may be madeup of a plurality of microprocessors or other processing devices whichmay or may not be coupled to one another. Likewise, where a memory isillustrated or described herein, the memory may be made up of aplurality of memory devices which may or may not be coupled to oneanother.

While certain exemplary embodiments have been described and shown in theaccompanying drawings, it is to be understood that such embodiments aremerely illustrative of, and not restrictive on, the broad invention, andthat this invention not be limited to the specific constructions andarrangements shown and described, since various other changes,combinations, omissions, modifications and substitutions, in addition tothose set forth in the above paragraphs, are possible. Those skilled inthe art will appreciate that various adaptations and modifications ofthe just described embodiments can be configured without departing fromthe scope and spirit of the invention. Therefore, it is to be understoodthat, within the scope of the appended claims, the invention may bepracticed other than as specifically described herein.

The invention claimed is:
 1. A system for improved modelling, the systemcomprising: a controller comprising one or more memory devices withcomputer-readable program code stored thereon, one or more communicationdevices connected to a network, and one or more processing devices,wherein the one or more processing devices execute the computer-readableprogram code to: collect electronic biometric data of a user from one ormore data sources comprising social media systems, third party vendorsystems, systems of known exposed data, and public information spacesystems, wherein the electronic biometric data of the user comprises atleast one of image data, video data, and voice recording data associatedwith the user; store the collected electronic biometric data of the useras a biometric account for the user in a personal library associatedwith the user, wherein the personal library associated with the usercomprises a real-time NoSQL database; cause a generative adversarialneural network system to determine improved biometric feature selectionand improved model refinements for existing biometric authenticationmodels based on the biometric account for the user in the personallibrary associated with the user; determine, based on the improvedbiometric feature selection and the improved model refinements for theexisting biometric authentication models, user exposure levels for oneor more biometric authentication channels, combinations of biometricauthentication channels, and/or combinations of non-biometric andbiometric authentication channels; and establish a custom adversarialstrategy for generative adversarial network (“GAN”) attacks based on thedetermined user exposure levels for the one or more biometricauthentication channels, combinations of biometric authenticationchannels, and/or combinations of non-biometric and biometricauthentication channels.
 2. The system of claim 1, wherein theprocessing devices further execute the computer-readable program codeto: identify, within the personal library associated with the user, oneor more inconsistencies in the stored electronic biometric data of theuser; remove the identified one or more inconsistencies in the storedelectronic biometric data of the user from the personal libraryassociated with the user; and consolidate the stored electronicbiometric data of the user within the personal library associated withthe user, without the identified one or more inconsistencies.
 3. Thesystem of claim 1, wherein the processing devices further execute thecomputer-readable program code to dynamically update the storedelectronic biometric data of the user within the personal libraryassociated with the user in real time in response to determining thatnew or adjusted electronic biometric data of the user is available fromthe one or more data sources.
 4. The system of claim 1, whereinestablishing the custom adversarial strategy for GAN attacks comprises:changing the existing biometric authentication models comprisingimproved model refinements as determined by the generative adversarialneural network system by requiring a biometric authentication actioninvolving traditionally or previously unexposed biometric features orscenarios.
 5. The system of claim 1, wherein establishing the customadversarial strategy for GAN attacks comprises: changing the existingbiometric authentication models comprising improved model refinements asdetermined by the generative adversarial neural network system byrequiring a randomly selected authentication action involving changedbiometric authentication conditions or interaction patterns.
 6. Thesystem of claim 1, wherein establishing the custom adversarial strategyfor GAN attacks comprises: changing weighted values of at least one ofthe one or more biometric authentication channels; or changing weightedvalues of at least one of the existing biometric authentication modelscomprising improved model refinements as determined by the generativeadversarial neural network system.
 7. The system of claim 1, whereinestablishing the custom adversarial strategy for GAN attacks comprises:adding one or more additional authentication methods to the existingbiometric authentication models comprising improved model refinements asdetermined by the generative adversarial neural network system; orrequiring a stepped up level of authentication from existingauthentication models.
 8. The system of claim 1, wherein establishingthe custom adversarial strategy for GAN attacks comprises: determiningwhether received biometric authentication data from an individualpurporting to be the user matches a data pattern present within a customdatabase of known GAN attack data.
 9. The system of claim 1, wherein theprocessing devices further execute the computer-readable program codeto: identify previous biometric authentication sessions for the userfrom a historical user database, where the previous biometricauthentication sessions involved the existing biometric authenticationmodels without the improved model refinements; and evaluate receivedbiometric authentication data of the user for each of the previousbiometric authentication sessions for the user based on the customadversarial strategy for GAN attacks to identify potential exposuresfrom previously unknown GAN attacks.
 10. The system of claim 9, whereinthe processing devices further execute the computer-readable programcode to, in response to identifying a first previous biometricauthentication session that is associated with a previously unknown GANattack, tag the received biometric authentication data of the user forthat first previous biometric authentication session as being associatedwith imitability and exposure metrics.
 11. A computer program productfor improved biometric authentication modelling, the computer programproduct comprising at least one non-transitory computer readable mediumcomprising computer readable instructions, the instructions comprisinginstructions for: collecting electronic biometric data of a user fromone or more data sources comprising social media systems, third partyvendor systems, systems of known exposed data, and public informationspace systems, wherein the electronic biometric data of the usercomprises at least one of image data, video data, and voice recordingdata associated with the user; storing the collected electronicbiometric data of the user as a biometric account for the user in apersonal library associated with the user, wherein the personal libraryassociated with the user comprises a real-time NoSQL database; causing agenerative adversarial neural network system to determine improvedbiometric feature selection and improved model refinements for existingbiometric authentication models based on the biometric account for theuser in the personal library associated with the user; determining,based on the improved biometric feature selection and the improved modelrefinements for the existing biometric authentication models, userexposure levels for one or more biometric authentication channels,combinations of biometric authentication channels, and/or combinationsof non-biometric and biometric authentication channels; and establishinga custom adversarial strategy for generative adversarial network (“GAN”)attacks based on the determined user exposure levels for the one or morebiometric authentication channels, combinations of biometricauthentication channels, and/or combinations of non-biometric andbiometric authentication channels.
 12. The computer program product ofclaim 11, wherein the computer readable instructions further compriseinstructions for: identifying, within the personal library associatedwith the user, one or more inconsistencies in the stored electronicbiometric data of the user; removing the identified one or moreinconsistencies in the stored electronic biometric data of the user fromthe personal library associated with the user; and consolidating thestored electronic biometric data of the user within the personal libraryassociated with the user, without the identified one or moreinconsistencies.
 13. The computer program product of claim 11, whereinthe computer readable instructions further comprise instructions fordynamically updating the stored electronic biometric data of the userwithin the personal library associated with the user in real time inresponse to determining that new or adjusted electronic biometric dataof the user is available from the one or more data sources.
 14. Thecomputer program product of claim 11, wherein establishing the customadversarial strategy for GAN attacks comprises: changing the existingbiometric authentication models comprising improved model refinements asdetermined by the generative adversarial neural network system byrequiring a biometric authentication action involving traditionally orpreviously unexposed biometric features or scenarios.
 15. The computerprogram product of claim 11, wherein establishing the custom adversarialstrategy for GAN attacks comprises: changing the existing biometricauthentication models comprising improved model refinements asdetermined by the generative adversarial neural network system byrequiring a randomly selected authentication action involving changedbiometric authentication conditions or interaction patterns.
 16. Thecomputer program product of claim 11, wherein establishing the customadversarial strategy for GAN attacks comprises: changing weighted valuesof at least one of the one or more biometric authentication channels; orchanging weighted values of at least one of the existing biometricauthentication models comprising improved model refinements asdetermined by the generative adversarial neural network system.
 17. Thecomputer program product of claim 11, wherein establishing the customadversarial strategy for GAN attacks comprises: adding one or moreadditional authentication methods to the existing biometricauthentication models comprising improved model refinements asdetermined by the generative adversarial neural network system; orrequiring a stepped up level of authentication from existingauthentication models.
 18. The computer program product of claim 11,wherein establishing the custom adversarial strategy for GAN attackscomprises: determining whether received biometric authentication datafrom an individual purporting to be the user matches a data patternpresent within a custom database of known GAN attack data.
 19. Acomputer implemented method for improved biometric authenticationmodelling, said computer implemented method comprising: providing acomputing system comprising a computer processing device and anon-transitory computer readable medium, where the computer readablemedium comprises configured computer program instruction code, such thatwhen said instruction code is operated by said computer processingdevice, said computer processing device performs the followingoperations: collecting electronic biometric data of a user from one ormore data sources comprising social media systems, third party vendorsystems, systems of known exposed data, and public information spacesystems, wherein the electronic biometric data of the user comprises atleast one of image data, video data, and voice recording data associatedwith the user; storing the collected electronic biometric data of theuser as a biometric account for the user in a personal libraryassociated with the user, wherein the personal library associated withthe user comprises a real-time NoSQL database; causing a generativeadversarial neural network system to determine improved biometricfeature selection and improved model refinements for existing biometricauthentication models based on the biometric account for the user in thepersonal library associated with the user; determining, based on theimproved biometric feature selection and the improved model refinementsfor the existing biometric authentication models, user exposure levelsfor one or more biometric authentication channels, combinations ofbiometric authentication channels, and/or combinations of non-biometricand biometric authentication channels; and establishing a customadversarial strategy for generative adversarial network (“GAN”) attacksbased on the determined user exposure levels for the one or morebiometric authentication channels, combinations of biometricauthentication channels, and/or combinations of non-biometric andbiometric authentication channels.